Deleting T-cells, normalization and integratin wnn
2022-02-07
1 Pre-processing
1.1 Load packages
library(Seurat)
library(Signac)
library(tidyverse)
library(ggplot2)
library(ggpubr)
library(magick)
library(knitr)
library(kableExtra)
library(devtools)
library(harmony)
library(patchwork)
library(kableExtra)1.2 Parameters
# Paths
path_to_obj <- ("~/Documents/multiome_tonsil_Lucia/results/R_objects/11.tonsil_multiome_integrated_without_doublets_normalized.rds")
path_to_save <- ("~/Documents/multiome_tonsil_Lucia/results/R_objects/12.tonsil_multiome_bcells_without_doublets_normalized.rds")1.3 Load data
tonsil_wnn_bcell <- readRDS(path_to_obj)vars <- str_subset(colnames(tonsil_wnn_bcell@meta.data), "^wsnn_res")
clusters_gg <- purrr::map(vars, function(x) {
p <- DimPlot(
tonsil_wnn_bcell,
group.by = x,
reduction = "wnn.umap",
pt.size = 0.1, label = T
)
p
})
clusters_gg## [[1]]
##
## [[2]]
##
## [[3]]
##
## [[4]]
##
## [[5]]
1.4 filtering T-cells
tonsil_wnn_bcell$is_tcell <-
tonsil_wnn_bcell$wsnn_res.0.01 == "1"
tonsil_wnn_bcell <- subset(tonsil_wnn_bcell, subset = is_tcell == FALSE)DimPlot(tonsil_wnn_bcell, reduction = "wnn.umap", group.by = "wsnn_res.0.01", label = TRUE, pt.size = 0.1)
1.5 scATAC
1.5.1 Normalization and linear dimensional reduction
We exclude the first dimension as this is typically correlated with sequencing depth Cells cluster completely separately in ATAC without harmony; so run harmony after SVD
RunSVD LSI
DefaultAssay(tonsil_wnn_bcell) <- "peaks"
tonsil_wnn_bcell <- RunTFIDF(tonsil_wnn_bcell)## Performing TF-IDF normalizationtonsil_wnn_bcell <- FindTopFeatures(tonsil_wnn_bcell, min.cutoff = "q0")
tonsil_wnn_bcell <- RunSVD(tonsil_wnn_bcell)## Running SVD## Scaling cell embeddings1.5.2 Plot the Depth correlation plot
Compute the correlation between total counts and each reduced dimension component.
LSI component is typically highly correlated with sequencing depth. The first LSI component often captures sequencing depth (technical variation) rather than biological variation. If this is the case, the component should be removed from downstream analysis. We can assess the correlation between each LSI component and sequencing depth using the DepthCor() function:
For scRNA-seq data we don’t typically observe such a strong relationship between the first PC and sequencing depth, and so usually retain the first PC in downstream analyses.
DepthCor(tonsil_wnn_bcell)
Here we see there is a very strong correlation between the first LSI component and the total number of counts for the cell, so we will perform downstream steps without this component.
1.5.3 UMAP representation
- dimensional reduction key, specifies the string before the number for the dimension names. UMAP by default
- reduction.name: Name to store dimensional reduction under in the Seurat object
tonsil_wnn_bcell <- RunUMAP(
tonsil_wnn_bcell,
dims = 2:40,
reduction = "lsi",
reduction.name = "umap.atac",
reduction.key = "atacUMAP_"
)## Warning: The default method for RunUMAP has changed from calling Python UMAP via reticulate to the R-native UWOT using the cosine metric
## To use Python UMAP via reticulate, set umap.method to 'umap-learn' and metric to 'correlation'
## This message will be shown once per session## 11:55:55 UMAP embedding parameters a = 0.9922 b = 1.112## 11:55:56 Read 46120 rows and found 39 numeric columns## 11:55:56 Using Annoy for neighbor search, n_neighbors = 30## 11:55:57 Building Annoy index with metric = cosine, n_trees = 50## 0% 10 20 30 40 50 60 70 80 90 100%## [----|----|----|----|----|----|----|----|----|----|## **************************************************|
## 11:56:14 Writing NN index file to temp file /var/folders/kz/y10np0cj213fybqz181z9lghbzr42p/T//RtmpP3rndT/file3fc26f8884bd
## 11:56:15 Searching Annoy index using 1 thread, search_k = 3000
## 11:56:54 Annoy recall = 100%
## 11:57:22 Commencing smooth kNN distance calibration using 1 thread
## 11:57:27 Initializing from normalized Laplacian + noise
## 11:57:31 Commencing optimization for 200 epochs, with 1988358 positive edges
## 11:58:04 Optimization finishedatac.umap<-DimPlot(
tonsil_wnn_bcell,
reduction = "umap.atac",
group.by = "library_name",
pt.size = 0.1
) + ggtitle('scATAC UMAP') + NoLegend()
atac.umap
#split_by: library ,edad, genero1.6 scRNA
1.6.1 Normalization and linear dimensional reduction-
1.6.2 NormalizeData
DefaultAssay(tonsil_wnn_bcell) <- "RNA"
tonsil_wnn_bcell <- NormalizeData(
tonsil_wnn_bcell,
normalization.method = "LogNormalize",
scale.factor = 1e4
)
tonsil_wnn_bcell <- tonsil_wnn_bcell %>%
FindVariableFeatures(nfeatures = 3000) %>%
ScaleData() %>%
RunPCA() PCAPlot(tonsil_wnn_bcell,
group.by = "library_name")
ElbowPlot(object = tonsil_wnn_bcell)
find variable genes
1.6.3 UMAP representation
tonsil_wnn_bcell <- RunUMAP(
tonsil_wnn_bcell,
dims = 1:30,
reduction = "pca",
reduction.name = "umap.rna",
reduction.key = "rnaUMAP_"
)## 12:00:32 UMAP embedding parameters a = 0.9922 b = 1.112## 12:00:32 Read 46120 rows and found 30 numeric columns## 12:00:32 Using Annoy for neighbor search, n_neighbors = 30## 12:00:32 Building Annoy index with metric = cosine, n_trees = 50## 0% 10 20 30 40 50 60 70 80 90 100%## [----|----|----|----|----|----|----|----|----|----|## **************************************************|
## 12:00:36 Writing NN index file to temp file /var/folders/kz/y10np0cj213fybqz181z9lghbzr42p/T//RtmpP3rndT/file3fc2448a7bba
## 12:00:36 Searching Annoy index using 1 thread, search_k = 3000
## 12:00:51 Annoy recall = 100%
## 12:00:52 Commencing smooth kNN distance calibration using 1 thread
## 12:00:55 Initializing from normalized Laplacian + noise
## 12:00:58 Commencing optimization for 200 epochs, with 2043478 positive edges
## 12:01:28 Optimization finishedrna.umap<-DimPlot(
tonsil_wnn_bcell,
reduction = "umap.rna",
group.by = "library_name",
pt.size = 0.1) + NoLegend() + ggtitle('scRNA UMAP')
rna.umap
atac.umap + rna.umap
hacer primero harmony, quitar batch effect. atac y rna. harmony
2 Run Harmony
Pass the Seurat object to the RunHarmony function and specify which variable to integrate out. A Seurat object is returned with corrected Harmony coordinates.
2.1 scATAC
DefaultAssay(tonsil_wnn_bcell) <- "peaks"
tonsil_wnn_bcell <- RunHarmony(
object = tonsil_wnn_bcell,
reduction = "lsi",
dims = 2:40,
group.by.vars = "library_name",
assay.use = "peaks",
project.dim = FALSE,
reduction.save = "harmony_peaks"
)## Harmony 1/10## Harmony 2/10## Harmony 3/10## Harmony 4/10## Harmony 5/10## Harmony 6/10## Harmony 7/10## Harmony 8/10## Harmony converged after 8 iterations2.1.1 UMAP representation
tonsil_wnn_bcell <- RunUMAP(
tonsil_wnn_bcell,
dims = 2:40,
reduction = "harmony_peaks",
reduction.name = "umap.atac",
reduction.key = "atacUMAP_"
)## 12:04:55 UMAP embedding parameters a = 0.9922 b = 1.112## 12:04:55 Read 46120 rows and found 39 numeric columns## 12:04:55 Using Annoy for neighbor search, n_neighbors = 30## 12:04:55 Building Annoy index with metric = cosine, n_trees = 50## 0% 10 20 30 40 50 60 70 80 90 100%## [----|----|----|----|----|----|----|----|----|----|## **************************************************|
## 12:05:00 Writing NN index file to temp file /var/folders/kz/y10np0cj213fybqz181z9lghbzr42p/T//RtmpP3rndT/file3fc27a76b6ea
## 12:05:00 Searching Annoy index using 1 thread, search_k = 3000
## 12:05:17 Annoy recall = 100%
## 12:05:18 Commencing smooth kNN distance calibration using 1 thread
## 12:05:20 Initializing from normalized Laplacian + noise
## 12:05:22 Commencing optimization for 200 epochs, with 2053750 positive edges
## 12:05:49 Optimization finishedHarm_peak<-DimPlot(
tonsil_wnn_bcell,
reduction = "umap.atac",
group.by = "library_name",
pt.size = 0.1
) + NoLegend() + ggtitle('Peak Harmony')3 scRNA
tonsil_wnn_bcell <- RunUMAP(
tonsil_wnn_bcell,
dims = 2:40,
reduction = "harmony_rna",
reduction.name = "umap.rna",
reduction.key = "rnaUMAP_"
)## 12:07:14 UMAP embedding parameters a = 0.9922 b = 1.112## 12:07:14 Read 46120 rows and found 39 numeric columns## 12:07:14 Using Annoy for neighbor search, n_neighbors = 30## 12:07:14 Building Annoy index with metric = cosine, n_trees = 50## 0% 10 20 30 40 50 60 70 80 90 100%## [----|----|----|----|----|----|----|----|----|----|## **************************************************|
## 12:07:18 Writing NN index file to temp file /var/folders/kz/y10np0cj213fybqz181z9lghbzr42p/T//RtmpP3rndT/file3fc26f5508a2
## 12:07:18 Searching Annoy index using 1 thread, search_k = 3000
## 12:07:34 Annoy recall = 100%
## 12:07:34 Commencing smooth kNN distance calibration using 1 thread
## 12:07:37 Initializing from normalized Laplacian + noise
## 12:07:39 Commencing optimization for 200 epochs, with 2100454 positive edges
## 12:08:07 Optimization finishedHarm_rna<-DimPlot(
tonsil_wnn_bcell,
reduction = "umap.rna",
group.by = "library_name",
pt.size = 0.1
) + NoLegend() + ggtitle('RNA Harmony')3.1 scATAC and RNAseq Harmony
Harm_peak+Harm_rna 
3.2 Joint UMAP visualization
FindNeighbors
Constructs a Shared Nearest Neighbor (SNN) Graph for a given dataset. We first determine the k-nearest neighbors of each cell. We use this knn graph to construct the SNN graph by calculating the neighborhood overlap (Jaccard index) between every cell and its k.param nearest neighbors.
# build a joint neighbor graph using both assays
tonsil_wnn_bcell <- FindMultiModalNeighbors(
object = tonsil_wnn_bcell,
reduction.list = list("harmony_peaks", "harmony_rna"),
dims.list = list(2:40, 1:30), modality.weight.name = "Joint_snn_umap"
)## Calculating cell-specific modality weights## Finding 20 nearest neighbors for each modality.## Calculating kernel bandwidths## Warning in FindMultiModalNeighbors(object = tonsil_wnn_bcell, reduction.list
## = list("harmony_peaks", : The number of provided modality.weight.name is not
## equal to the number of modalities. peaks.weight RNA.weight are used to store the
## modality weights## Finding multimodal neighbors## Constructing multimodal KNN graph## Constructing multimodal SNN graph# build a joint UMAP visualization
tonsil_wnn_bcell <- RunUMAP(
object = tonsil_wnn_bcell,
nn.name = "weighted.nn",
reduction.name = "wnn.umap",
reduction.key = "wnnUMAP_")## 12:10:39 UMAP embedding parameters a = 0.9922 b = 1.112## 12:10:41 Commencing smooth kNN distance calibration using 1 thread## 12:10:43 Initializing from normalized Laplacian + noise## 12:10:45 Commencing optimization for 200 epochs, with 1475168 positive edges## 12:11:15 Optimization finishedjoint.umap<- DimPlot(tonsil_wnn_bcell, label = FALSE, group.by = "library_name", pt.size = 0.1, reduction = "wnn.umap") + plot_annotation(title = 'Joint UMAP')+ ggtitle('Joint UMAP by library name') + NoLegend()
joint.umap
joint.umap_age<- DimPlot(tonsil_wnn_bcell, label = FALSE, split.by = "age_group", pt.size = 0.1, reduction = "wnn.umap") + plot_annotation(title = 'Joint UMAP ')+ ggtitle('Joint UMAP age ')
joint.umap_hospital<- DimPlot(tonsil_wnn_bcell, label = FALSE, split.by = "hospital", pt.size = 0.1, reduction = "wnn.umap") + plot_annotation(title = 'Joint UMAP ')+ ggtitle('Joint UMAP age ')
joint.umap_age
joint.umap_hospital
3.3 FindClusters
#find cluster algorithm 3 = SLM algorithm
tonsil_wnn_bcell <- FindClusters(tonsil_wnn_bcell, resolution = c(0.005,0.01,0.05,0.075,0.1,0.25,0.5,0.75),algorithm = 3, graph.name = "wsnn",verbose = FALSE)
print(colnames(tonsil_wnn_bcell@meta.data))## [1] "lib_name_barcode" "orig.ident" "nCount_RNA"
## [4] "nFeature_RNA" "nCount_ATAC" "nFeature_ATAC"
## [7] "nucleosome_signal" "nucleosome_percentile" "TSS.enrichment"
## [10] "TSS.percentile" "tss.level" "percent.mt"
## [13] "percent_ribo" "nCount_peaks" "nFeature_peaks"
## [16] "library_name" "donor_id" "sex"
## [19] "age" "age_group" "hospital"
## [22] "assay" "barcodes" "doublet_scores"
## [25] "predicted_doublets" "peaks.weight" "RNA.weight"
## [28] "wsnn_res.0.005" "wsnn_res.0.01" "seurat_clusters"
## [31] "sub.cluster_0.25" "sub.cluster0_0.5" "is_doublet"
## [34] "wsnn_res.0.05" "wsnn_res.0.75" "wsnn_res.0.075"
## [37] "is_tcell" "wsnn_res.0.1" "wsnn_res.0.25"
## [40] "wsnn_res.0.5"vars <- str_subset(colnames(tonsil_wnn_bcell@meta.data), "^wsnn_res")
clusters_gg <- purrr::map(vars, function(x) {
p <- DimPlot(
tonsil_wnn_bcell,
group.by = x,
reduction = "wnn.umap",
pt.size = 0.1, label = T
)
p
})
clusters_gg## [[1]]
##
## [[2]]
##
## [[3]]
##
## [[4]]
##
## [[5]]
##
## [[6]]
##
## [[7]]
##
## [[8]]
Idents(tonsil_wnn_bcell)<-"wsnn_res.0.05"
tonsil_markers_05<-FindAllMarkers(object = tonsil_wnn_bcell, only.pos = TRUE)## Calculating cluster 0## Calculating cluster 1## Calculating cluster 2## Calculating cluster 3## Calculating cluster 4## Calculating cluster 5## Calculating cluster 6write.csv(tonsil_markers_05,file=paste0("~/Documents/multiome_tonsil_Lucia/results/tables/", "tonsil_markers_bcell_05.csv"))3.3.1 Get top n for each cluster
Resolution 0.01
tonsil_markers_05 %>% group_by(cluster) %>% top_n(n = 10, wt = avg_log2FC) %>% write.csv(.,file=paste0("~/Documents/multiome_tonsil_Lucia/results/tables/", "top10_tonsil_markers_bcell_05.csv"))
tonsil_markers_05 %>% group_by(cluster) %>% top_n(n = 5, wt = avg_log2FC) %>% write.csv(.,file=paste0("~/Documents/multiome_tonsil_Lucia/results/tables/", "top5_tonsil_markers_bcell_05.csv"))
top5_tonsil_markers_05<-tonsil_markers_05 %>% group_by(cluster) %>% top_n(n = 5, wt = avg_log2FC)
top10_tonsil_markers_05<-tonsil_markers_05 %>% group_by(cluster) %>% top_n(n = 10, wt = avg_log2FC)df_top5<-as.data.frame(top5_tonsil_markers_05)
kbl(df_top5,caption = "Table of the top 5 marker of each cluster resolution 0.005") %>%
kable_paper("striped", full_width = F)| p_val | avg_log2FC | pct.1 | pct.2 | p_val_adj | cluster | gene |
|---|---|---|---|---|---|---|
| 0 | 2.439037 | 0.994 | 0.768 | 0 | 0 | BANK1 |
| 0 | 2.310305 | 0.618 | 0.170 | 0 | 0 | COL19A1 |
| 0 | 2.002782 | 0.806 | 0.357 | 0 | 0 | CAMK1D |
| 0 | 1.857727 | 0.729 | 0.316 | 0 | 0 | MAML2 |
| 0 | 1.791176 | 0.639 | 0.227 | 0 | 0 | PARP15 |
| 0 | 2.918946 | 0.955 | 0.166 | 0 | 1 | HMGB2 |
| 0 | 2.798930 | 0.967 | 0.254 | 0 | 1 | TUBA1B |
| 0 | 2.694975 | 0.967 | 0.262 | 0 | 1 | H2AFZ |
| 0 | 2.422139 | 0.851 | 0.335 | 0 | 1 | HIST1H4C |
| 0 | 2.412212 | 0.815 | 0.043 | 0 | 1 | TOP2A |
| 0 | 2.612691 | 0.835 | 0.260 | 0 | 2 | MAML3 |
| 0 | 2.542482 | 0.983 | 0.299 | 0 | 2 | AC023590.1 |
| 0 | 2.459488 | 0.819 | 0.165 | 0 | 2 | AC104170.1 |
| 0 | 2.257369 | 0.929 | 0.250 | 0 | 2 | RAPGEF5 |
| 0 | 2.156023 | 0.796 | 0.249 | 0 | 2 | LHFPL2 |
| 0 | 2.505368 | 0.907 | 0.042 | 0 | 3 | FYB1 |
| 0 | 2.485483 | 0.816 | 0.038 | 0 | 3 | INPP4B |
| 0 | 2.066960 | 0.726 | 0.010 | 0 | 3 | THEMIS |
| 0 | 1.998146 | 0.902 | 0.147 | 0 | 3 | PRKCH |
| 0 | 1.978889 | 0.807 | 0.010 | 0 | 3 | BCL11B |
| 0 | 5.751547 | 0.498 | 0.151 | 0 | 4 | IGHGP |
| 0 | 5.587754 | 0.697 | 0.326 | 0 | 4 | IGHG3 |
| 0 | 5.666937 | 0.960 | 0.918 | 0 | 4 | IGKC |
| 0 | 5.984929 | 0.648 | 0.439 | 0 | 4 | IGHA1 |
| 0 | 5.614213 | 0.896 | 0.762 | 0 | 4 | IGLC2 |
| 0 | 4.211622 | 0.735 | 0.056 | 0 | 5 | SLC8A1 |
| 0 | 3.942508 | 0.687 | 0.009 | 0 | 5 | LYZ |
| 0 | 3.486982 | 0.687 | 0.086 | 0 | 5 | AOAH |
| 0 | 3.469585 | 0.197 | 0.014 | 0 | 5 | SPRR3 |
| 0 | 3.357581 | 0.695 | 0.004 | 0 | 5 | PLXDC2 |
| 0 | 4.279665 | 0.976 | 0.081 | 0 | 6 | LINC01374 |
| 0 | 3.938582 | 0.920 | 0.011 | 0 | 6 | LINC01478 |
| 0 | 3.801066 | 0.992 | 0.142 | 0 | 6 | RUNX2 |
| 0 | 3.708258 | 0.932 | 0.009 | 0 | 6 | FAM160A1 |
| 0 | 3.361241 | 0.880 | 0.004 | 0 | 6 | DACH1 |
4 Markers exploration
markers_gg <- function(x){purrr::map(x, function(x) {
p <- FeaturePlot(
tonsil_wnn_bcell,
features = x,
reduction = "wnn.umap",
pt.size = 0.1
)
p
})}MARKERS
Immature B cells express CD19, CD 20, CD34, CD38, and CD45R, T-cell receptor/CD3 complex (TCR/CD3 complex)
- T-cells (identified by high expression of CD3D and CD3E).
- monocytes (identified by high expression of LYZ and S100A8).
- naive B-cells (identified by high expression of CD79A, CD79B and BLNK).
- plasma cells (identified by B-cell and proliferation markers, such as TOP2A or MKI67).
- poor-quality cells (identified by high mitochondrial expression). If a cell has pores in the membrane due to cell lysis, the cytosolic mRNA will leak out of the cell; but if the diameter of mitochondria is higher than the pores, they will get trapped inside the cell.
DZ: SUGCT, CXCR4, AICDA
LZ: CD83, BCL2A1
GC total: MEF2B, BCL6, IRF4
PC: PRDM1, SLAMF7, MZB1, FKBP11
monocytes_markers<-c("LYZ","S100A8")
naive_markers<-c("CD79A", "CD79B", "BLNK")
bib_Bcell_markers<-c("CD19","CR2","MS4A1","RALGPS2","CD79A")
bib_Tcell_markers<-c("CD3E","CD4","CD8A","FOXP3","IL17A")
markers_bcell<-c("BANK1","ARHGAP24","ADAM28","MARCH1","RAPGEF5","AFF2","RGS13","LPP","IGHG1","IGLC1","SLC8A1","LYZ","PLXDC2","FAM160A1","IGHA1","IGLC2", "SETBP1","ENTPD1","COL19A1","CCSER1")
markers_tcell<-c("INPP4B","FYB1","LEF1","IL7R","IL6ST","CCL5","GNLY","NKG7","DTHD1","RUNX2", "FOXP3","CD8A","IL17A","CD2")4.1 Bibliography markers
4.1.1 T-cells
CD8+ T cell markers:“CD3D”, “CD8A” NK cell markers:“GNLY”, “NKG7”
markers_gg (bib_Tcell_markers)## [[1]]
##
## [[2]]
##
## [[3]]
##
## [[4]]
##
## [[5]]
4.1.2 B cells
markers_gg(bib_Bcell_markers)## [[1]]
##
## [[2]]
##
## [[3]]
##
## [[4]]
##
## [[5]]
4.1.2.1 Monocytes
markers_gg(monocytes_markers)## [[1]]
##
## [[2]]
4.1.2.2 Naive
markers_gg (naive_markers)## [[1]]
##
## [[2]]
##
## [[3]]
4.1.3 Markers
m<-c("PRDM1","XBP1","IRF4","MEF2B","BCL6")
DZ<-c("SUGCT", "CXCR4", "AICDA")
LZ<- c("CD83","BCL2A1")
GC<- c("MEF2B", "BCL6","IRF4")
PC<- c("PRDM1","SLAMF7", "MZB1", "FKBP11")
markers_gg(m)## [[1]]
##
## [[2]]
##
## [[3]]
##
## [[4]]
##
## [[5]]
4.1.3.1 Dark Zone
markers_gg(DZ)## [[1]]
##
## [[2]]
##
## [[3]]
4.1.3.2 Light Zone
markers_gg(LZ)## [[1]]
##
## [[2]]
4.1.3.3 Germinal Center
markers_gg(GC)## [[1]]
##
## [[2]]
##
## [[3]]
4.1.3.4 Plasma cell
markers_gg(PC)## [[1]]
##
## [[2]]
##
## [[3]]
##
## [[4]]
5 Save
saveRDS(tonsil_wnn_bcell, path_to_save)6 Session Information
sessionInfo()## R version 4.1.2 (2021-11-01)
## Platform: x86_64-apple-darwin17.0 (64-bit)
## Running under: macOS Big Sur 10.16
##
## Matrix products: default
## BLAS: /Library/Frameworks/R.framework/Versions/4.1/Resources/lib/libRblas.0.dylib
## LAPACK: /Library/Frameworks/R.framework/Versions/4.1/Resources/lib/libRlapack.dylib
##
## locale:
## [1] es_ES.UTF-8/es_ES.UTF-8/es_ES.UTF-8/C/es_ES.UTF-8/es_ES.UTF-8
##
## attached base packages:
## [1] stats graphics grDevices utils datasets methods base
##
## other attached packages:
## [1] patchwork_1.1.1 harmony_0.1.0 Rcpp_1.0.8 devtools_2.4.3
## [5] usethis_2.1.5 kableExtra_1.3.4 knitr_1.36 magick_2.7.3
## [9] ggpubr_0.4.0 forcats_0.5.1 stringr_1.4.0 dplyr_1.0.7
## [13] purrr_0.3.4 readr_2.1.1 tidyr_1.1.4 tibble_3.1.6
## [17] ggplot2_3.3.5 tidyverse_1.3.1 Signac_1.5.0 SeuratObject_4.0.4
## [21] Seurat_4.0.6 BiocStyle_2.22.0
##
## loaded via a namespace (and not attached):
## [1] utf8_1.2.2 reticulate_1.22 tidyselect_1.1.1
## [4] htmlwidgets_1.5.4 grid_4.1.2 docopt_0.7.1
## [7] BiocParallel_1.28.3 Rtsne_0.15 munsell_0.5.0
## [10] codetools_0.2-18 ica_1.0-2 future_1.23.0
## [13] miniUI_0.1.1.1 withr_2.4.3 colorspace_2.0-2
## [16] highr_0.9 rstudioapi_0.13 stats4_4.1.2
## [19] ROCR_1.0-11 ggsignif_0.6.3 tensor_1.5
## [22] listenv_0.8.0 labeling_0.4.2 slam_0.1-49
## [25] GenomeInfoDbData_1.2.7 polyclip_1.10-0 farver_2.1.0
## [28] rprojroot_2.0.2 parallelly_1.30.0 vctrs_0.3.8
## [31] generics_0.1.1 xfun_0.29 lsa_0.73.2
## [34] ggseqlogo_0.1 R6_2.5.1 GenomeInfoDb_1.30.0
## [37] cachem_1.0.6 bitops_1.0-7 spatstat.utils_2.3-0
## [40] assertthat_0.2.1 promises_1.2.0.1 scales_1.1.1
## [43] gtable_0.3.0 globals_0.14.0 processx_3.5.2
## [46] goftest_1.2-3 rlang_0.4.12 systemfonts_1.0.3
## [49] RcppRoll_0.3.0 splines_4.1.2 rstatix_0.7.0
## [52] lazyeval_0.2.2 spatstat.geom_2.3-1 broom_0.7.10
## [55] BiocManager_1.30.16 yaml_2.2.1 reshape2_1.4.4
## [58] abind_1.4-5 modelr_0.1.8 backports_1.4.1
## [61] httpuv_1.6.4 tools_4.1.2 bookdown_0.24
## [64] ellipsis_0.3.2 spatstat.core_2.3-2 jquerylib_0.1.4
## [67] RColorBrewer_1.1-2 BiocGenerics_0.40.0 sessioninfo_1.2.2
## [70] ggridges_0.5.3 plyr_1.8.6 zlibbioc_1.40.0
## [73] RCurl_1.98-1.5 prettyunits_1.1.1 ps_1.6.0
## [76] rpart_4.1-15 deldir_1.0-6 pbapply_1.5-0
## [79] cowplot_1.1.1 S4Vectors_0.32.3 zoo_1.8-9
## [82] haven_2.4.3 ggrepel_0.9.1 cluster_2.1.2
## [85] fs_1.5.2 magrittr_2.0.1 RSpectra_0.16-0
## [88] data.table_1.14.2 scattermore_0.7 lmtest_0.9-39
## [91] reprex_2.0.1 RANN_2.6.1 SnowballC_0.7.0
## [94] fitdistrplus_1.1-6 matrixStats_0.61.0 pkgload_1.2.4
## [97] hms_1.1.1 mime_0.12 evaluate_0.14
## [100] xtable_1.8-4 sparsesvd_0.2 readxl_1.3.1
## [103] IRanges_2.28.0 gridExtra_2.3 testthat_3.1.1
## [106] compiler_4.1.2 KernSmooth_2.23-20 crayon_1.4.2
## [109] htmltools_0.5.2 mgcv_1.8-38 later_1.3.0
## [112] tzdb_0.2.0 lubridate_1.8.0 DBI_1.1.2
## [115] tweenr_1.0.2 dbplyr_2.1.1 MASS_7.3-54
## [118] Matrix_1.3-4 car_3.0-12 cli_3.1.1
## [121] parallel_4.1.2 igraph_1.2.10 GenomicRanges_1.46.1
## [124] pkgconfig_2.0.3 plotly_4.10.0 spatstat.sparse_2.1-0
## [127] xml2_1.3.3 svglite_2.0.0 bslib_0.3.1
## [130] webshot_0.5.2 XVector_0.34.0 rvest_1.0.2
## [133] callr_3.7.0 digest_0.6.29 sctransform_0.3.2
## [136] RcppAnnoy_0.0.19 spatstat.data_2.1-2 Biostrings_2.62.0
## [139] rmarkdown_2.11 cellranger_1.1.0 leiden_0.3.9
## [142] fastmatch_1.1-3 uwot_0.1.11 shiny_1.7.1
## [145] Rsamtools_2.10.0 lifecycle_1.0.1 nlme_3.1-153
## [148] jsonlite_1.7.3 carData_3.0-4 limma_3.50.0
## [151] desc_1.4.0 viridisLite_0.4.0 fansi_1.0.2
## [154] pillar_1.6.5 lattice_0.20-45 pkgbuild_1.3.0
## [157] fastmap_1.1.0 httr_1.4.2 survival_3.2-13
## [160] remotes_2.4.2 glue_1.6.1 qlcMatrix_0.9.7
## [163] png_0.1-7 ggforce_0.3.3 stringi_1.7.6
## [166] sass_0.4.0 memoise_2.0.1 irlba_2.3.5
## [169] future.apply_1.8.1